Recall that in the Bennett–Brassard key distribution scheme, after Alice and Bob have obtained bit strings that ideally are supposed to be identical, they have to do some further processing to make sure they end up with strings that really are identical. Our first goal in this chapter is to show how they can do this. As you might expect, we will use error-correcting codes of the sort we have been discussing in the preceding chapter. However, the way we use error-correcting codes in quantum key distribution is not quite the same as in classical communication. Normally, one corrects errors by encoding one's message into special codewords that are sufficiently different from each other that they will still be distinguishable after passing through a noisy channel. But in quantum key distribution the “noise” of the channel might actually be the effect of an eavesdropper who is free to manipulate the signals sent by Alice. Ordinary error correction is not designed for such a setting. So instead of using codewords to encode the original transmission, we wait until all the bits have been sent and then use an error-correcting code after the fact . In this respect error correction in quantum key distribution is similar to the use of an error-correcting code in the “hat problem” discussed at the end of Chapter 4. There also, the error-correcting code is applied only after all the data – in that case the vector of hat colors – has been generated and conveyed to the participants.
Recall that in the Bennett–Brassard key distribution scheme, after Alice and Bob have obtained bit strings that ideally are supposed to be identical, they have to do some further processing to make sure they end up with strings that really are identical. Our first goal in this chapter is to show how they can do this. As you might expect, we will use error-correcting codes of the sort we have been discussing in the preceding chapter. However, the way we use error-correcting codes in quantum key distribution is not quite the same as in classical communication. Normally, one corrects errors by encoding one's message into special codewords that are sufficiently different from each other that they will still be distinguishable after passing through a noisy channel. But in quantum key distribution the “noise” of the channel might actually be the effect of an eavesdropper who is free to manipulate the signals sent by Alice. Ordinary error correction is not designed for such a setting. So instead of using codewords to encode the original transmission, we wait until all the bits have been sent and then use an error-correcting code after the fact . In this respect error correction in quantum key distribution is similar to the use of an error-correcting code in the “hat problem” discussed at the end of Chapter 4. There also, the error-correcting code is applied only after all the data – in that case the vector of hat colors – has been generated and conveyed to the participants.
Quantum cryptography is essentially the quantum key distribution (QKD). In the context of QKD, one from two partners (Alice) generates and sends a sequence of qubits through a private quantum channel to another partner (Bob) and Bob receives the sequence and measures the state of each qubit. After the quantum transmission stage, Alice and Bob have almost identical qubit sequences. The erros are due to physical imperfections in the channel and presence of an eavesdropper. The next stage in QKD is key reconciliation (i.e. finding and correcting discrepancies between Alice's string and that of Bob). This reconciliation can be done by public discussion. Let us suppose there is a secret quantum channel between Alice and Bob through which Alice transmits a n-bit string A=(A1, A2,...,An)ε{0,1}n. Then Bob receives a n-bit string B=(B1, B2,...,Bn)ε{0,1)n. The string B differs from A due to the presence of noise and eavesdropper in the channel. One can estimate the bit error probability in the channel. For example, Bob can choose a random subset from his string and send it to Alice in public. Then Alice compares the received string with her corresponding subset and calculates the total number of protocol steps. The cascade scheme uses the interaction over the public channel to correct the secret strings by dividing them into the blocks of a fixed length. The length is determined from the bit error probability. A simple interactive routine is applied in each of these blocks. An error found in some block results in some action with other blocks. It is important to optimize the error-finding routines in standalone blocks as well as to organize the effective constrution of blocks with the object of protocol benchmark, information leakage and number of interactions between partners.
In the preceding chapter we mentioned the inevitable errors that occur when one tries to send quantum signals over, say, an optical fiber, even when there is no eavesdropper. But errors in transmission are not a problem just for quantum cryptography. For this entire chapter we forget about sending quantum information and instead focus on simply transmitting ordinary data faithfully over some kind of channel. Moreover, we assume that the data either is not sensitive or has already been encrypted. Unfortunately, many methods for transmitting data are susceptible to outside influences that can cause errors. How do we protect information from these errors? Error-correcting codes provide a mathematical method of not only detecting these errors, but also correcting them. Nowadays error-correcting codes are ubiquitous; they are used, for example, in cell-phone transmissions and satellite links, in the representation of music on a compact disk, and even in the bar codes in grocery stores. The story of modern error-correcting codes began with Claude Shannon's famous paper “A Mathematical Theory of Communication,” which was published in 1948. Shannon worked for Bell Labs where he specialized in finding solutions to problems that arose in telephone communication. Quite naturally, he started considering ways to correct errors that occurred when information was transmitted over phone lines. Richard Hamming, who also worked at Bell Labs on this problem, published a groundbreaking paper in 1950 on the subject.
Cryptography is a method of secure communication between two or more parties. The crucial step is exchanging a key in a secure manner. There are, however, two problems with conventional cryptography. First the sender and the receiver should exchange the key through highly reliable and secure channels. The second problem is that a clever eavesdropper can, by a careful analysis of the sent information, reconstruct the key. In this chapter, schemes to overcome these problems are presented. First a scheme for exchanging a key over public channels, the so-called RSA algorithm, is discussed. Then the protocols for the quantum key distribution (QKD), the Bennett–Brassard-84 (BB-84) and Bennett-92(B-92) protocols, are then presented. The QKD protocols are exclusively derived using Bohr’s principle of complementarity. An application of these ideas to the design of secure quantum money is discussed.
Introduction Error-correcting codes were introduced in the 1940s in order to implement a theorem of Shannon's [260] which guaranteed that virtually error-free communication could be obtained even over a noisy channel. The message to be communicated is first “encoded”, i.e. turned into a codeword, by adding "redundancy". The codeword is then sent through the channel and the received message is “decoded” by the receiver into a message resembling, as closely as possible, the original message. The degree of resemblance will depend on how good the code is in relation to the channel. The usual pictorial representation of this communication link is shown in the schematic diagram Figure 2.1. Here a message is first given by the source to the encoder that turns the message into a codeword, i.e. a string of letters from some alphabet, chosen according to the code used; the encoded message is then sent through the channel, where it may be subjected to noise and hence altered; this possibly-altered message, when it arrives at the decoder belonging to the receiver, is, first of all, equated with the most likely codeword, i.e. the one (should that exist) that, in a probabilistic sense depending on the channel, was probably sent; finally this “most likely” codeword is decoded and the message is passed on to the receiver. Example 2.1.1 Suppose we use an alphabet of just two symbols, 0 and 1, and we have only two messages, for example “no” corresponding to 0, and "yes” corresponding to 1. We wish to send the message “no”, and we add redundancy by simply repeating the message five times. Thus we encode the message as the codeword (00000). The channel interferes, perhaps, with the message and could, for example, change it to (01100). The decoder assesses the message and decides that of the two possible codewords, i.e. (00000) and (11111), the former is the more likely, and hence the message is decoded, correctly, as “no”. Notice that we have made several assumptions here: for example we have assumed that the probability of an error at any position in the word is less than, that each codeword is equally likely to be sent, and that the receiver is aware of the code used.
The authors consider error correction in quantum key distribution. To avoid that Alice and Bob unwittingly end up with different keys, precautions must be taken. Before running the error correction protocol, Bob and Alice normally sacrifice some bits to estimate the error rate. To reduce the probability that they end up with different keys to an acceptable level, the authors show that a large number of bits must be sacrificed. Instead, if Alice and Bob can make a good guess about the error rate before the error correction, they can verify that their keys are similar after the error correction protocol. In this study, the authors show that this verification can be done by utilising properties of low‐density parity check codes used in the error correction. The authors compare the different methods and show that by verification it is often possible to sacrifice less bits without compromising security. The improvement is heavily dependent on the error rate and the block length, but for a key produced by the IdQuantique system Clavis 2 , the increase in the key rate is ∼5%. The authors also show that for systems with large fluctuations in the error rate, a combination of the two methods is optimal.
In the current thesis we first talk about the six-qubit quantum error-correcting code and show its connections to entanglement-assisted error-correcting coding theory and then to subsystem codes. This code bridges the gap between the five-qubit (perfect) and Steane codes. We discuss two methods to encode one qubit into six physical qubits. Each of the two examples corrects an arbitrary single-qubit error. The first example is a degenerate six-qubit quantum error-correcting code. We prove that a six-qubit code without entanglement assistance cannot simultaneously possess a Calderbank-Shor-Steane (CSS) stabilizer and correct an arbitrary single-qubit error. A corollary of this result is that the Steane seven-qubit code is the smallest single-error correcting CSS code. Our second example is the construction of a non-degenerate six-qubit CSS entanglement-assisted code. This code uses one bit of entanglement (an ebit) shared between the sender (Alice) and the receiver (Bob) and corrects an arbitrary single-qubit error. In the second half of this thesis we explore the yet uncharted and relatively undiscovered area of quantum steganography. Steganography is the process of hiding secret information by embedding it in an innocent message. We present protocols for hiding quantum information in a codeword of a quantum error-correcting code passing through a channel. Using either a shared classical secret key or shared entanglement Alice disguises her information as errors in the channel. Bob can retrieve the hidden information, but an eavesdropper (Eve) with the power to monitor the channel, but without the secret key, cannot distinguish the message from channel noise. We analyze how difficult it is for Eve to detect the presence of secret messages, and estimate rates of steganographic communication and secret key consumption for certain protocols.